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Routing attacks detection in MANET using trust management enabled hybrid machine learning

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Abstract

The ever-changing topology in mobile ad hoc networks (MANETs) makes routing a formidable obstacle. The infrastructure-independent capabilities of MANET ensure the temporary communications linkages, but the lack of a good centralized monitoring method makes routing in MANETs a severe trust and safety concern. As a result, this study presents a new energy-and trust-aware protocol for routing that depends on the suggested as well as enabled by Deep Reinforcements Learning. The best route choice is being carried out by the suggested Dolphin Cat Optimizer according to the modelled objective function that takes into account trust criteria, including current trust, historic trust, both direct and indirect trust, delay, distance, and connection lifespan. Combining the advantages of both the Dolphins Echolocation as well as the Cat Swarm Optimization algorithms, the Dolphin Cat Optimizer is able to achieve quicker worldwide cooperation. The suggested protocol for routing achieved 0.6531, 0.0107, 0.3267, as well as 0.9898 in absence of network assaults, as well as 0.7693, 0.0112, 0.3605, as well as 0.9961 in the event of network attacks, according to the modeling involving 75 nodes.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, India

    G. Arulselvan

  2. Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, India

    A. Rajaram

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Arulselvan, G., Rajaram, A. Routing attacks detection in MANET using trust management enabled hybrid machine learning. Wireless Netw 31, 1481–1495 (2025). https://doi.org/10.1007/s11276-024-03846-7

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